\begin{abstract}
Data deduplication is important  for snapshot backup of virtual machines (VMs)
because of excessive redundant content.  Fingerprint search for source-side duplicate 
detection is resource intensive when the backup service for VMs  is co-located with  other 
cloud services.  This paper presents the design and analysis of
a fast VM-centric backup service with  a tradeoff 
for a competitive deduplication efficiency while using small computing resources, 
suitable for running on a converged cloud architecture that cohosts  many other services.
The  design consideration includes 
VM-centric file system block management for the increased VM snapshot availability.
%and  similarity-guided local search for  the improved overall deduplication effectiveness. 
%deduplication under popular chunks with extra replication support.
%and use an approximate method for fast and simplified snapshot deletion.
%by separating popular chunks and associating  file system blocks with one VM for non-popular chunks.
This  paper  describes an evaluation of this VM-centric scheme 
to assess  its deduplication efficiency, resource usage,  and fault tolerance.

% Collocating a cluster-based duplicate service with other cloud services
%   necessary to eliminate
%redundant blocks and reduce cost. Collocating a cluster-based duplicate service with other cloud services

%A cloud environment that hosts a large number of virtual machines (VMs) has
%a high storage demand for frequent backup of system image snapshots. 
%Deduplication of data blocks is necessary to eliminate
%redundant blocks and reduce cost. Collocating a cluster-based duplicate service with other cloud services
%reduces network traffic;
%however, 
\end{abstract}
